We investigated phase-sensitive optical coherence tomography's performance to monitor the dynamic changes during controlled heating of the bone. The results demonstrated the potential of this method to be used as feedback for the irrigation system.
Lasers have introduced many advantages to the medical field of osteotomy (bone cutting), however, they are not without drawbacks. The thermal side effects of laser osteotomy, in particular, affect a patient’s healing process. Employing an irrigation system during surgery is a standard solution for reducing thermal damage to the surrounding tissue, but, due to the high absorption peak of water at the wavelength of Er:YAG laser (2.96 μm), accumulated water acts as a blocking layer and reduces the ablation efficiency. Therefore, irrigation systems would benefit from a high-speed and accurate feedback system to monitor the temperature changes in the tissue of interest. Phase-sensitive optical coherence tomography (PhS-OCT) is a highly sensitive method for measuring internal displacement (photothermal-induced expansion) during laser surgery. In this study, we utilized the integrated swept source PhS-OCT system (operating at a central wavelength of 1314 nm and with an imaging-speed of 104,000 A-scans/s) with an Er:YAG laser to detect localized phase changes induced by laser ablation irradiation and thereby quantify the photothermal-induced expansion of bone. The PhS-OCT system was calibrated by measuring the phase changes corresponding to the displacement of cover glass attached to a piezoelectric actuator (PA4HEW, Thorlabs) at different operating voltages. Furthermore, we explored how the induced photothermal expansion of bone changes when irradiated by different pulse energies. Using a PhS-OCT system to spatially and temporally resolve measurements of axial displacement of bone during laser surgery can play an important role in determining the corresponding temperature map, which can, in turn, offer feedback to the irrigation system in smart laser osteotomy.
Optical Coherence Tomography (OCT) has been proven to be a precise monitoring tool for laserosteotomy which can provide three-dimensional, high resolution and real-time images of a target sample. However, the main technical drawback of utilizing OCT as a monitoring system for laser ablation is the limited imaging range. In this paper, we reported a prototype where we integrated a long-range swept-source OCT system (3.3cm imaging range in the air) with an Er:YAG laser for ablation. We demonstrated that the integrated system can monitor the ablation of bone by Er:YAG with varying pulse energy levels and durations.
Automatic tissue classification using optical coherence tomography (OCT) explores the possibility to control laser ablation in prevention for collateral damage of critical tissues. During ablation, tissue experience thermal dissipation which induces mechanical expansion and optical properties alteration. We reconstructed OCT images of bone, fat, and muscle tissues for pre and post ablation temperatures condition using Monte Carlo simulation. We trained a deep neural network to recognize tissue type based on reconstructed OCT images with pre-ablation temperature condition and tested it on post-ablation temprature condition. The reconstructed images show small changes in the tissue structure but do not significantly affect the performance of the classifier.
One of the most common image denoising technique used in Optical Coherence Tomography (OCT) is the frame averaging method. Inherent to this method is that the more images are used, the better the resulting image. This approach comes, however, at the price of increased acquisition time and introduced sensitivity to motion artifacts. To overcome these limitations, we proposed an artificial neural network architecture able to imitate an averaging method using only a single image frame. The reconstructed image has an improvement in the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) parameters compared to the original image. Additionally, we also observed an improvement in the sharpness of the denoised images. This result shows the possibility to use this method as a pre-processing step for real-time tissue classification in smart laser surgery especially in bone surgery.
The aim of this study is to develop an automatic tissue characterization system, based on Optical Coherence Tomography (OCT) images, for smart laser surgery. OCT is rapidly becoming the method of choice for investigating thin tissues or subsurface imaging. In smart laser surgery, OCT could be used to indicate which tissue is being irradiated, thereby preventing the laser from ablating critical tissue such as nerves and veins. Automatic tissue characterization based on the OCT images should be sufficient to give feedback to the laser control. In this study, two main neural networks were trained to classify texture and optical attenuation of three different tissues (bone, fat, and muscle). One neural network texture classifier was trained to differentiate between patterned and patternless images. The other neural network was trained to classify patternless images based on their attenuation profile. The two neural networks were stacked as a binary tree. The ability of this hybrid deep-learning approach to characterize tissue was evaluated for accuracy in classifying OCT images from these three different tissues. The overall (averaged) accuracy was 82.4% for the texture-based network and 98.0% for the attenuation-based (A-Scan) network. The fully connected layer of the neural network achieved 98.7% accuracy. This method shows the ability of the neural network to learn feature representation from OCT images and offers a feasible solution to the challenge of heuristic independent tissue characterization for histology and use in smart laser surgery.
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